Do Not Hesitate to Use Tversky—and Other Hints for Successful Active Analogue Searches with Feature Count Descriptors

2013 ◽  
Vol 53 (7) ◽  
pp. 1543-1562 ◽  
Author(s):  
Dragos Horvath ◽  
Gilles Marcou ◽  
Alexandre Varnek
Keyword(s):  
Feature Count

QSAR using momentum-space and trivial feature count descriptors – An application to Tetrahymena pyriformis toxicity

2009 ◽  
Vol 901 (1-3) ◽  
pp. 56-59 ◽  
Author(s):  
Jabir H.A. Al-Fahemi ◽  
David L. Cooper ◽  
Neil L. Allan
Keyword(s):  
Momentum Space ◽  
Tetrahymena Pyriformis ◽  
Feature Count

scholarly journals GENAVi: a shiny web application for gene expression normalization, analysis and visualization

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Alberto Luiz P. Reyes ◽  
Tiago C. Silva ◽  
Simon G. Coetzee ◽  
Jasmine T. Plummer ◽  
Brian D. Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Data Analyst ◽  
Reproducible Analysis ◽  
Programming Knowledge ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Feature Count

Abstract Background The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. Results We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. Conclusion The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

scholarly journals Clinical Data Classification With Noisy Intermediate Scale Quantum Computers

2021 ◽  
Author(s):  
Sasan Moradi ◽  
Christoph Brandner ◽  
Clemens Spielvogel ◽  
Denis Krajnc ◽  
Stefan Hillmich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Classification ◽  
Predictive Performance ◽  
Small Sample ◽  
Inner Product ◽  
Support Vector ◽  
Quantum Computers ◽  
Intermediate Scale ◽  
Quantum Machine Learning ◽  
Feature Count

Abstract Quantum machine learning has experienced a significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique (\({\text{log}}_{2}N\)) for embedding classical data into quantum states and estimating the inner product on 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the \({\text{log}}_{2}N\) encoding increases predictive performance with up to +2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.

scholarly journals Single Cell scale RNA-seq Analysis Protocol to analyze Smart-3SEQ data from RAGP neurons of pig heart

2021 ◽  
Author(s):  
Lakshmi Kuttippurathu ◽  
Alison Moss ◽  
Rajanikanth Vadigepalli
Keyword(s):  
Single Cell ◽  
Right Atrial ◽  
Rna Seq ◽  
Low Input ◽  
Batch Correction ◽  
Rnaseq Data ◽  
Analysis Workflow ◽  
Downstream Analysis ◽  
Ganglionated Plexus ◽  
Feature Count

Abstract The present protocol describes transcriptome mapping, data normalization and analysis pipeline with detailed steps for each of these aspects for single cell/ low input RNASeq data from Right Atrial Ganglionated Plexus (RAGP) of pig heart. The protocol with minor modifications can be adapted for low input samples with short reads or samples with low quality input RNA. Single cell samples acquired using Laser Capture Microdissection (LCM) were processed for RNA-Seq library preparation using Smart-3SEQ technique (Foley et al 2019). The data analysis workflow consists of (a) pre-processing- data trimming, read alignment and feature count and (b) downstream analysis- annotation, batch correction, filtering and normalization. The entire protocol is performed using freely available packages. Most of them are available within the R framework.

Gödel's Arithmetization Procedure as the Basis for a Pattern Recognition System: A Nascent Theory Mapping Pattern into Number and Number into Phenomenal Experience

1983 ◽  
Vol 57 (3_suppl) ◽  
pp. 1135-1159 ◽  
Author(s):  
Willard L. Brigner
Keyword(s):  
Recognition System ◽  
Prime Numbers ◽  
Stimulus Configuration ◽  
Phenomenal Experience ◽  
Input Matrix ◽  
System A ◽  
Pattern Processing ◽  
The Matrix ◽  
Feature Count ◽  
The Relationship

Prime numbers are used to code various dimensions of an input matrix (receptor surface), i.e., prime numbers code the position of each cell in the matrix, the position of each column and row constituted by the cells of the matrix, and the orientation of each such column or row. The coding permits any pattern or stimulus configuration to be changed into a single, unique number, viz., the serial product of the prime numbers which code the relevant dimensions of the pattern. Storage of a pattern is effected by storage of the serial product. By factoring the serial product, the pattern or stimulus configuration is analyzed into the dimensions (features) specified by the code. The factorization is also utilized in abstracting a schema, in approximating a feature count model, and in presenting a strategy for holistic vs sequential pattern processing. Finally, the relationship between the serial product representing a pattern and the phenomenal experience of a pattern is explored.

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